「神經電影學（neurocinematics）」結合了功能性磁振造影（functional magnetic resonance imaging, fMRI）與電影研究，自 2008 年提出以來，提供了新的視角來解析電影內容與觀影體驗的神經基礎，並促進以動態、自然情境之刺激（naturalistic stimuli）探討跨個體的大腦同步性與大腦網路模組化的研究。另一方面，隨著「開放神經科學（open neuroscience）」的興起，資料的重複使用與再分析成為可能，可提升研究的可驗證性與可信賴度。本研究是結合上述兩點，利用一組開放的神經影像資料集（每部電影受試者數 6 至 20 人），以 Yeo 等人（2011）的 17個大腦功能網路為基礎，檢驗跨受試者相似性（inter-subject similarity, ISS）與受試者內模組性（intra-subject modularity, Q）的動態關係。結果顯示，在完整電影的層次上，並未觀察到 Betzel 等人（2020）所提出「高 ISS 伴隨模組化下降與跨系統整合增加，而低 ISS 則對應較高的分化（segregation）與近似靜息態的網路結構」；相反地，我們發現整體ISS 與 Q 呈現正相關，當跨受試者的腦反應更同步時，個體內的模組化程度亦隨之提升。即便在極端條件下（最高與最低 3 個 ISS 時段），亦未重現 Betzel 等人提出之假說。當電影被切割為較短片段時，每部片僅在少數時段觀察到與 Betzel 一致的現象。並且，在具高度辨識的經典電影場景，ISS 與 Q 並未穩定地呈現反向關係，顯示即便觀眾在注意力上高度同步，個體間的大腦處理策略仍展現差異與認知複雜性。本研究的貢獻在於將 Betzel 等人以短片段驗證的 ISS–Q 框架延伸至完整電影，並發現其結果並非完全適用。透過使用開放資料，本研究不僅檢驗了既有假設的再現性，也拓展了其適用性，凸顯自然情境刺激在理解人類大腦動態網路組織中的重要性。

Neurocinematics, the integration of functional magnetic resonance imaging (fMRI) with film studies, provides a powerful framework for investigating the neural basis of cinematic experience under naturalistic conditions. While prior work has linked inter-subject similarity (ISS) to shifts in brain modularity, Betzel et al. (2020) suggested that high ISS coincides with reduced modularity and increased integration across networks. In this study, we re-examined this relationship using an open fMRI dataset (6–20 participants per film) and the 17 functional networks defined by Yeo et al. (2011). Across ten full-length films, we consistently observed a positive correlation between ISS and intra-subject modularity (Q): stronger neural synchronization across viewers was accompanied by higher modular organization within individuals. Even under extreme ISS conditions, Betzel’s inverse relationship did not replicate. When films were divided into shorter segments, the predicted inverse ISS–Q pattern emerged only in isolated periods. Moreover, iconic film scenes eliciting strong attentional alignment did not reliably produce an inverse relationship, suggesting that synchronized attention does not necessarily translate into uniform network integration strategies. By extending the ISS–Q framework from short clips to full-length films, this study demonstrates its limited generalizability and underscores the complexity of neural dynamics during naturalistic viewing. Leveraging open data, we highlight both the reproducibility challenges of existing models and the importance of naturalistic stimuli for advancing our understanding of dynamic brain network organization.

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